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Biologically Reasoned Point-of-Interest Image Compression for Mobile Robots

  • M. Podpora
  • J. Sadecki
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 60)

Abstract

In this paper authors describe image compression based on the idea of biological “yellow spot” in which the quality/resolution is variable, depending on the distance from the point-of-interest. Reducing the amount of data in a robot’s vision system enables to use a computer cluster for non-time-critical “mental” processing tasks like “memories” or “associations”. This approach can be particularly useful in HTM-based data processing of robot’s vision system data.

Keywords

Mobile Robot Object Recognition Image Compression Threshold Function Computer Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Hecht, E.: Optics, 4th edn. Addison-Wesley, San Francisco (2002)Google Scholar
  2. 2.
    Hawkins, J., Dileep, G.: Hierarchical temporal memory- concepts, theory and terminology. Numenta Inc. (2007), http://www.numenta.com/Numenta_HTM_Concepts.pdf (accessed March 2, 2009)
  3. 3.
    Hawkins, J., Blakeslee, S.: On intelligence. Times Books, New York (2004)Google Scholar
  4. 4.
    Hawkins, J.: Learn like a human. IEEE Spectrum 44(7) (2007), http://www.spectrum.ie-ee.org/apr07/4982 (accessed March 2, 2009)
  5. 5.
    Mallat, S.: A wavelet tour of signal processing. Academic Press, San Diego (1999)zbMATHGoogle Scholar
  6. 6.
    Podpora, M.: Biologically reasoned machine vision: RLE vs. entropy-coding compression of DWT-transformed images. In: Proc. EEICT Conference, Brno (2008)Google Scholar
  7. 7.
    Numenta Inc. Numenta Pictures Demonstration Program (2007), http://www.numenta.com/about-numenta/technology/pictures-demo.php (accessed March 2, 2009)
  8. 8.
    Sadecki, J.: Parallel optimization algorithms and investigation of their efficiency: parallel distributed memory systems. Internal Report Opole University of Technology, Opole (2001) (in Polish)Google Scholar
  9. 9.
    Baker, M.: Cluster Computing White Paper (2001), http://arxiv.org/abs/cs/0004014 (accessed March 2, 2009)
  10. 10.
    Nałęcz, M., Duch, W., Korbicz, J., Rutkowski, L., Tadeusiewicz, R.: Biocybernetics and biomedical engineering neural networks. Akademicka Oficyna Wydawnicza Exit, Warsaw 6 (2000) (in Polish)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • M. Podpora
    • 1
  • J. Sadecki
    • 1
  1. 1.Department of Electrical and Computer EngineeringOpole University of TechnologyOpolePoland

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